statsmodels
No security findings. Scanned 1 time, most recently 2026-10-21.
Second scanner: NVIDIA SkillSpector
Independent static run, no language model, over the same files as the Cisco scan above. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
Fits and diagnoses Python statistical models including OLS, GLM, discrete and mixed models, ARIMA and SARIMAX. Supports coefficient inference, marginal effects, model comparison and time series forecasting with explicit design and uncertainty checks. Used for econometrics and statistical modeling; for guided test selection with APA reporting, see statistical-analysis.
- Package
- 10 files, 128 KB
- SKILL.md
- 1492 words, 12+ sections
- Code
- None. Instructions only.
- Allowed tools
ReadWriteEditBash- License
- BSD-3-Clause license
- Compatibility
- Requires Python 3.10+ and statsmodels 0.15.0; the tested NumPy 2.5.3/SciPy 1.18.1 stack needs Python 3.12+. Plotting needs matplotlib; predictive metrics need scikit-learn. Network access is needed only for installation or documentation; no credentials.
SKILL.md covers: Statsmodels: Statistical Modeling and Econometrics · Overview · Current Compatibility · When to Use This Skill · Quick Start, Capabilities, and Model Selection · Best Practices · Data Preparation · Model Building · Inference · Model Evaluation · Reporting · Common Workflows
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 12 KB |
references/discrete_choice.md | doc | 18 KB |
references/glm.md | doc | 18 KB |
references/linear_models.md | doc | 14 KB |
references/model_selection.md | doc | 3.4 KB |
references/modeling_capabilities.md | doc | 6.1 KB |
references/quick_start_guide.md | doc | 4.7 KB |
references/review.md | doc | 6.9 KB |
references/stats_diagnostics.md | doc | 24 KB |
references/time_series.md | doc | 21 KB |
External hosts referenced
www.statsmodels.org arxiv.org github.com docs.scipy.org doi.org export.arxiv.org
Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.
Package contents recorded 2026-10-22 at commit 593377bb0487. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.
What we checked
- Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
- Bytecode analyzer.
- Pipeline analyzer.
- Correlation analyzer.
- Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
- Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.
What we did not check
- Runtime behaviour. The skill was not executed; findings come from reading its files.
- Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
- Content the analyzers cannot read: images, binaries, and some non-English text.
- Known scanner bypass classes. See the methodology and limitations page.
Sources
- Repository: https://github.com/K-Dense-AI/scientific-agent-skills
- Raw scan result: skills-statsmodels-scan.json
- Badge for your README:
- Request a re-scan